Balancing Student Success: Assessing Supplemental Instruction Through Coarsened Exact Matching
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Supplemental Instruction (SI) is a voluntary, non-remedial, peer-facilitated, course-specific intervention that has been widely demonstrated to increase student success, yet concerns persist regarding the biasing effects of disproportionate participation by already higher-performing students. With a focus on maintaining access for all students, a large, public university in the Western United States used student demographic, performance, and SI participation data to evaluate the intervention’s efficacy while reducing selection bias. This analysis was conducted in the first year of SI implementation within a traditionally high-challenge introductory psychology course. Findings indicate a statistically significant relationship between student participation in SI and increased odds of successful course completion. Furthermore, the application of Coarsened Exact Matching reduced concerns that increased course performance was attributed to an over-representation of higher performing students who elected to attend SI Sessions.
KeywordsLearning analytics High impact practice Program assessment Propensity score matching
- Arendale, D. (1997). Supplemental Instruction (SI): Review of research concerning the effectiveness of SI from the University of Missouri-Kansas City and other institutions from across the United States. In S. Mioduski & G. Enright (Eds.), Proceedings of the 17th and 18th annual institutes for learning assistance professionals: 1996 and 1997. Tucson: University Learning Center, University of Arizona.Google Scholar
- Blackwell, M., Iacus, S., King, G., & Porro, G. (2009). CEM: coarsened exact matching in Stata. The Stata Journal, 9, 524–546.Google Scholar
- Creswell, J. W., Plano Clark, V. L., Gutmann, M., & Hanson, W. (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research. Thousand Oaks, CA: Sage.Google Scholar
- International Center for Supplemental Instruction. (2014). Supplemental Instruction supervisor manual. Kansas City, MO.Google Scholar
- King, G., & Nielsen, R. (2016). Why propensity scores should not be used for matching. Working paper.Google Scholar
- Laumakis, M., Graham, C., & Dziuban, C. (2009). The Sloan-C pillars and boundary objects as a framework for evaluating blended learning. Journal of Asynchronous Learning Networks, 13(1), 75–87.Google Scholar
- Martin, D., & Arendale, D. (1993). Supplemental instruction: Improving first-year student success in high-risk courses (2nd ed.). Columbia: National Resource Center for the First Year Experience and Students in Transition, University of South Carolina.Google Scholar
- R Core Team. (2016). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org.